Development of Artificial Intelligence-Based Programs for the Diagnosis of Myocarditis in COVID-19 Using Chest Computed Tomography Data»

  • Ievgen A. Nastenko National Amosov Institute of Cardiovascular Surgery of the National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine; National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-1076-9337
  • Maksym O. Honcharuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0003-1537-4198
  • Vitalii O. Babenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine https://orcid.org/0000-0002-8433-3878
  • Mykola I. Lynnyk Yanovskyi National Scientific Center of Phthisiatry, Pulmonology and Allergology of the National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0002-0011-7482
  • Viktoria I. Ignatieva Yanovskyi National Scientific Center of Phthisiatry, Pulmonology and Allergology of the National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0003-0604-4349
  • Vitalii A. Yachnyk Yanovskyi National Scientific Center of Phthisiatry, Pulmonology and Allergology of the National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0003-0424-1947
DOI: https://doi.org/10.30702/ujcvs/24.32(03)/NH052-5865
Keywords: COVID-19, medical image analysis, texture analysis, modelling, machine learning, artificial intelligence, ensemble methods

Abstract

It has been established that 7.2% of patients hospitalized with coronavirus disease (COVID-19) exhibit signs of heart disease, with 23% of these patients experiencing heart failure. Currently, there is a lack of data on chest computed tomography (CT) for diagnosing myocarditis associated with COVID-19.

The aim. To justify the feasibility and develop classification models for diagnosing myocarditis in COVID-19 patients based on chest CT data processing.

Materials and methods. A retrospective analysis of data from 140 COVID-19 patients was conducted. Chest CT scans were analyzed using DRAGONFLY software, with permission from Object Research Systems. The COVID-CT-MD database, which includes CT data from 169 confirmed cases of SARS-CoV-2 infection, was used to build classification models. The regions of interest were fragments of heart CT images. Texture analysis methods were employed to create diagnostic models.

Results. It was shown that the average density of the myocardium of a patient with a confirmed diagnosis of SARS-CoV-2 infection according to the Hounsfield scale does not essentially differ from the densitometric indicators of a healthy person. Therefore, the research was focused on finding structural changes in CT images for their use in constructing diagnostic models.

The use of different classification algorithms had little effect on classification accuracy, probably due to the informational content of the input data. However, the obtained accuracy of the diagnostic models is acceptable and allows them to be used to support medical decision-making regarding diagnosis and treatment.

Conclusions. Using classic methods, myocarditis was diagnosed in 7.1% of patients with severe pneumonia caused by the coronavirus. The global data closely aligns with the results of our clinical studies. The obtained results allowed for assessing structural changes in the myocardium characteristic of the acute form of SARS-CoV-2 infection. The constructed classification models indicate that specific changes in the myocardium during the acute form of SARS-CoV-2 infection can be identified using CT. The highest diagnostic accuracy on test samples reached 74%. The implementation of the developed diagnostic programs based on texture analysis of CT data and artificial intelligence technologies enables the diagnosis of myocarditis and the assessment of long-term treatment efficiency. Creation of these diagnostic programs using artificial intelligence technologies significantly simplifies the work of radiologists and improves the efficiency of myocarditis diagnosis in patients with SARS-CoV-2 infection.

References

  1. Puntmann VO, Carerj ML, Wieters I, Fahim M, Arendt C, Hoffmann J, et al. Outcomes of Cardiovascular Magnetic Resonance Imaging in Patients Recently Recovered From Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020;5(11):1265-1273. https://doi.org/10.1001/jamacardio.2020.3557
  2. Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the cardiovascular system. Nat Rev Cardiol. 2020;17(5):259-260. https://doi.org/10.1038/s41569-020-0360-5
  3. Imazio M, Klingel K, Kindermann I, Brucato A, De Rosa FG, Adler Y, et al. COVID-19 pandemic and troponin: indirect myocardial injury, myocardial inflammation or myocarditis? Heart. 2020;106(15):1127-1131. https://doi.org/10.1136/heartjnl-2020-317186
  4. Babapoor-Farrokhran S, Gill D, Walker J, Rasekhi RT, Bozorgnia B, Amanullah A. Myocardial injury and COVID-19: Possible mechanisms. Life Sci. 2020;253:117723. https://doi.org/10.1016/j.lfs.2020.117723
  5. Tavazzi G, Pellegrini C, Maurelli M, Belliato M, Sciutti F, Bottazzi A, et al. Myocardial localization of coronavirus in COVID-19 cardiogenic shock. Eur J Heart Fail. 2020;22(5):911-915. https://doi.org/10.1002/ejhf.1828
  6. Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al.Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420-422. https://doi.org/10.1016/S2213-2600(20)30076-X
  7. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. https://doi.org/10.1016/s0140-6736(20)30183-5
  8. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med.2020;46(5):846-848. https://doi.org/10.1007/s00134-020-05991-x
  9. Liu PP, Blet A, Smyth D, Li H. The Science Underlying COVID-19: Implications for the Cardiovascular System. Circulation. 2020;142(1):68-78. https://doi.org/10.1161/CIRCULATIONAHA.120.047549
  10. Ferreira VM, Schulz-Menger J, Holmvang G, Kramer CM, Carbone I, Sechtem U, et al. Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. J Am Coll Cardiol. 2018;72(24):3158-3176. https://doi.org/10.1016/j.jacc.2018.09.072
  11. Driggin E, Madhavan MV, Bikdeli B, Chuich T, Laracy J, Biondi-Zoccai G, et al. Cardiovascular Considerations for Patients, Health Care Workers, and Health Systems During the COVID-19 Pandemic. J Am Coll Cardiol. 2020;75(18):2352-2371. https://doi.org/10.1016/j.jacc.2020.03.031
  12. Society for Cardiovascular Magnetic Resonance. SCMR’S COVID-19 Preparedness Toolkit. SCMR;2024 [cited 2024 Jul 31]. Available from: https://scmr.org/page/COVID19
  13. Afshar P, Heidarian S, Enshaei N, Naderkhani F, Rafiee MJ, Oikonomou A, et al. COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning. Sci Data. 2021;8(1):121. https://doi.org/10.1038/s41597-021-00900-3
  14. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Trans Syst Man Cybern. 1973;SMC-3(6):610-621. https://doi.org/10.1109/tsmc.1973.4309314
  15. Honcharuk M, Nastenko I, Linnik M. [The effectiveness of the use of computed tomography and modern information technologies in detecting structural changes of the heart in the acute form of COVID-19]. Biomedical Engineering and Technology. 2024;(14):47-53. Ukrainian. https://doi.org/10.20535/2617-8974.2024.14.304054
  16. BabenkoV, Nastenko I, Pavlov V, Horodetska O, Dykan I, Tarasiuk B, et al. Classification of Pathologies on Medical Images Using the Algorithm of Random Forest of Optimal-Complexity Trees. Cybern Syst Anal. 2023;59:346-358. https://doi.org/10.1007/s10559-023-00569-z
  17. Svitailo VS, Chemych MD, Saienko OS. [Long-covid and associated injuries of the cardiovascular and nervoussystems]. Infectious Diseases. 2022;(4):49-54. Ukrainian. https://doi.org/10.11603/1681-2727.2022.4.13701
Published
2024-09-27
How to Cite
Nastenko, I. A., Honcharuk, M. O., Babenko, V. O., Lynnyk, M. I., Ignatieva, V. I., & Yachnyk, V. A. (2024). Development of Artificial Intelligence-Based Programs for the Diagnosis of Myocarditis in COVID-19 Using Chest Computed Tomography Data». Ukrainian Journal of Cardiovascular Surgery, 32(3), 58-65. https://doi.org/10.30702/ujcvs/24.32(03)/NH052-5865
Issue
Ukrainian Journal of Cardiovascular Surgery Vol 32 No 3 2024
Section
MYOCARDIAL PATHOLOGY AND HEART FAILURE